Overview

Dataset statistics

Number of variables11
Number of observations10430
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory138.0 B

Variable types

Numeric10
Categorical1

Alerts

modular_ratio is highly correlated with ratioHigh correlation
weight is highly correlated with peak_numberHigh correlation
peak_number is highly correlated with weightHigh correlation
ratio is highly correlated with modular_ratioHigh correlation
upper_margin is highly correlated with interlinear_spacingHigh correlation
modular_ratio is highly correlated with ratioHigh correlation
interlinear_spacing is highly correlated with upper_marginHigh correlation
ratio is highly correlated with modular_ratioHigh correlation
modular_ratio is highly correlated with ratioHigh correlation
ratio is highly correlated with modular_ratioHigh correlation
intercolumnar_distance is highly correlated with row_numberHigh correlation
upper_margin is highly correlated with lower_margin and 4 other fieldsHigh correlation
lower_margin is highly correlated with upper_margin and 3 other fieldsHigh correlation
row_number is highly correlated with intercolumnar_distance and 1 other fieldsHigh correlation
modular_ratio is highly correlated with upper_margin and 4 other fieldsHigh correlation
interlinear_spacing is highly correlated with upper_margin and 4 other fieldsHigh correlation
weight is highly correlated with upper_marginHigh correlation
peak_number is highly correlated with upper_margin and 3 other fieldsHigh correlation
ratio is highly correlated with modular_ratio and 1 other fieldsHigh correlation
class is highly correlated with row_numberHigh correlation
upper_margin is highly skewed (γ1 = 91.76218687) Skewed
interlinear_spacing is highly skewed (γ1 = 22.13946265) Skewed

Reproduction

Analysis started2022-09-22 23:55:09.896442
Analysis finished2022-09-22 23:56:06.946630
Duration57.05 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

intercolumnar_distance
Real number (ℝ)

HIGH CORRELATION

Distinct144
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0008524439118
Minimum-3.498799
Maximum11.819916
Zeros1
Zeros (%)< 0.1%
Negative4434
Negative (%)42.5%
Memory size81.6 KiB
2022-09-22T19:56:07.615810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.498799
5-th percentile-0.585651
Q1-0.128929
median0.043885
Q30.204355
95-th percentile0.661077
Maximum11.819916
Range15.318715
Interquartile range (IQR)0.333284

Descriptive statistics

Standard deviation0.9914313305
Coefficient of variation (CV)1163.045822
Kurtosis40.47992399
Mean0.0008524439118
Median Absolute Deviation (MAD)0.172814
Skewness2.512437908
Sum8.89099
Variance0.9829360831
MonotonicityNot monotonic
2022-09-22T19:56:08.701367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.498799308
 
3.0%
0.15498286
 
2.7%
0.080916272
 
2.6%
0.130292271
 
2.6%
0.117948265
 
2.5%
-0.042522265
 
2.5%
0.019197251
 
2.4%
0.142636241
 
2.3%
-0.00549236
 
2.3%
0.031541228
 
2.2%
Other values (134)7807
74.9%
ValueCountFrequency (%)
-3.498799308
3.0%
-3.4864554
 
< 0.1%
-3.4617686
 
0.1%
-3.437086
 
0.1%
-3.4123925
 
< 0.1%
-3.05442111
 
0.1%
-2.8075446
 
0.1%
-2.5730119
 
0.1%
-2.52363518
 
0.2%
-2.474264
 
< 0.1%
ValueCountFrequency (%)
11.8199168
0.1%
9.9436519
0.1%
9.523969
0.1%
8.3142638
0.1%
5.7590877
0.1%
4.9690810
0.1%
4.5247029
0.1%
4.46298316
0.2%
3.72235212
0.1%
3.26562916
0.2%

upper_margin
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct208
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03361059195
Minimum-2.426761
Maximum386
Zeros0
Zeros (%)0.0%
Negative5922
Negative (%)56.8%
Memory size81.6 KiB
2022-09-22T19:56:09.802371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.426761
5-th percentile-0.620989
Q1-0.259834
median-0.055704
Q30.203385
95-th percentile0.572391
Maximum386
Range388.426761
Interquartile range (IQR)0.463219

Descriptive statistics

Standard deviation3.920868463
Coefficient of variation (CV)116.6557396
Kurtosis9007.991724
Mean0.03361059195
Median Absolute Deviation (MAD)0.227685
Skewness91.76218687
Sum350.558474
Variance15.3732095
MonotonicityNot monotonic
2022-09-22T19:56:10.851574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.189174206
 
2.0%
-0.291239173
 
1.7%
-0.220579157
 
1.5%
0.069915149
 
1.4%
0.289748143
 
1.4%
-0.338346140
 
1.3%
-0.055704136
 
1.3%
-0.071406135
 
1.3%
0.203385134
 
1.3%
-0.063555133
 
1.3%
Other values (198)8924
85.6%
ValueCountFrequency (%)
-2.426761130
1.2%
-2.39535614
 
0.1%
-2.0891612
 
0.1%
-1.96354113
 
0.1%
-1.94783914
 
0.1%
-1.91643416
 
0.2%
-1.68089916
 
0.2%
-1.6494948
 
0.1%
-1.30404212
 
0.1%
-1.29619113
 
0.1%
ValueCountFrequency (%)
3861
 
< 0.1%
43.1336561
 
< 0.1%
19.4701883
 
< 0.1%
17.5702024
 
< 0.1%
16.9656622
 
< 0.1%
13.8958483
 
< 0.1%
12.6553628
0.1%
10.653316
0.1%
9.6562111
0.1%
8.133081
 
< 0.1%

lower_margin
Real number (ℝ)

HIGH CORRELATION

Distinct231
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0005253415149
Minimum-3.210528
Maximum50
Zeros0
Zeros (%)0.0%
Negative1737
Negative (%)16.7%
Memory size81.6 KiB
2022-09-22T19:56:12.088596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.210528
5-th percentile-3.210528
Q10.064919
median0.217845
Q30.352988
95-th percentile0.537921
Maximum50
Range53.210528
Interquartile range (IQR)0.288069

Descriptive statistics

Standard deviation1.12020198
Coefficient of variation (CV)-2132.330966
Kurtosis386.1347465
Mean-0.0005253415149
Median Absolute Deviation (MAD)0.142256
Skewness7.474409689
Sum-5.479312
Variance1.254852476
MonotonicityNot monotonic
2022-09-22T19:56:13.743564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.210528627
 
6.0%
0.214288143
 
1.4%
0.239183143
 
1.4%
0.388552135
 
1.3%
0.14316124
 
1.2%
0.139604121
 
1.2%
0.306755119
 
1.1%
0.299642119
 
1.1%
0.352988117
 
1.1%
0.349432115
 
1.1%
Other values (221)8667
83.1%
ValueCountFrequency (%)
-3.210528627
6.0%
-3.20697115
 
0.1%
-3.20341532
 
0.3%
-3.07538518
 
0.2%
-2.97580523
 
0.2%
-2.95802316
 
0.2%
-2.90823412
 
0.1%
-2.4814658
 
0.1%
-2.34987818
 
0.2%
-2.32498313
 
0.1%
ValueCountFrequency (%)
501
 
< 0.1%
7.4586815
< 0.1%
7.4195613
 
< 0.1%
6.3810911
 
< 0.1%
6.2601733
 
< 0.1%
5.4919912
0.1%
5.1968096
0.1%
5.0830049
0.1%
4.3290474
 
< 0.1%
3.94139911
0.1%

exploitation
Real number (ℝ)

Distinct750
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.002386667881
Minimum-5.440122
Maximum3.987152
Zeros0
Zeros (%)0.0%
Negative4859
Negative (%)46.6%
Memory size81.6 KiB
2022-09-22T19:56:14.980333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-5.440122
5-th percentile-1.815842
Q1-0.528002
median0.095763
Q30.65821
95-th percentile1.401386
Maximum3.987152
Range9.427274
Interquartile range (IQR)1.186212

Descriptive statistics

Standard deviation1.008526572
Coefficient of variation (CV)-422.5667843
Kurtosis3.342667849
Mean-0.002386667881
Median Absolute Deviation (MAD)0.606907
Skewness-0.9248334086
Sum-24.892946
Variance1.017125846
MonotonicityNot monotonic
2022-09-22T19:56:15.800961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.44012243
 
0.4%
-0.52725629
 
0.3%
-0.18441724
 
0.2%
0.13908724
 
0.2%
0.33425823
 
0.2%
0.71135223
 
0.2%
-1.07815623
 
0.2%
-1.98108523
 
0.2%
-0.81737222
 
0.2%
0.95572522
 
0.2%
Other values (740)10174
97.5%
ValueCountFrequency (%)
-5.44012243
0.4%
-3.4418376
 
0.1%
-3.0188539
 
0.1%
-2.98634
 
< 0.1%
-2.96395116
 
0.2%
-2.9413648
 
0.1%
-2.83224616
 
0.2%
-2.8098089
 
0.1%
-2.70122712
 
0.1%
-2.6360618
 
0.1%
ValueCountFrequency (%)
3.98715213
0.1%
2.9743591
 
< 0.1%
2.7913929
0.1%
2.25863312
0.1%
2.21119113
0.1%
2.12358615
0.1%
2.04633614
0.1%
2.0418916
0.2%
2.0214513
 
< 0.1%
2.00405517
0.2%

row_number
Real number (ℝ)

HIGH CORRELATION

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.006369532215
Minimum-4.922215
Maximum1.066121
Zeros1
Zeros (%)< 0.1%
Negative1539
Negative (%)14.8%
Memory size81.6 KiB
2022-09-22T19:56:16.678174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-4.922215
5-th percentile-1.347089
Q10.17234
median0.261718
Q30.261718
95-th percentile0.976743
Maximum1.066121
Range5.988336
Interquartile range (IQR)0.089378

Descriptive statistics

Standard deviation0.9920533183
Coefficient of variation (CV)155.7497921
Kurtosis14.80829878
Mean0.006369532215
Median Absolute Deviation (MAD)0.089378
Skewness-3.701355076
Sum66.434221
Variance0.9841697864
MonotonicityNot monotonic
2022-09-22T19:56:17.759364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.2617185210
50.0%
0.172341920
 
18.4%
0.976743565
 
5.4%
0.082961500
 
4.8%
-4.922215265
 
2.5%
0.351096167
 
1.6%
-0.006417131
 
1.3%
0.887365116
 
1.1%
-1.257711112
 
1.1%
-1.078955112
 
1.1%
Other values (38)1332
 
12.8%
ValueCountFrequency (%)
-4.922215265
2.5%
-4.83283710
 
0.1%
-4.7434594
 
< 0.1%
-4.6540819
 
0.1%
-3.8496776
 
0.1%
-3.31340810
 
0.1%
-3.2240313
 
0.1%
-3.1346528
 
0.1%
-3.04527419
 
0.2%
-2.77713913
 
0.1%
ValueCountFrequency (%)
1.06612141
 
0.4%
0.976743565
 
5.4%
0.887365116
 
1.1%
0.79798788
 
0.8%
0.70860992
 
0.9%
0.61923102
 
1.0%
0.52985235
 
0.3%
0.44047454
 
0.5%
0.351096167
 
1.6%
0.2617185210
50.0%

modular_ratio
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct226
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01397286385
Minimum-7.450257
Maximum53
Zeros0
Zeros (%)0.0%
Negative5482
Negative (%)52.6%
Memory size81.6 KiB
2022-09-22T19:56:18.912292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-7.450257
5-th percentile-1.429155
Q1-0.598658
median-0.058835
Q30.564038
95-th percentile1.685209
Maximum53
Range60.450257
Interquartile range (IQR)1.162696

Descriptive statistics

Standard deviation1.126245103
Coefficient of variation (CV)80.60230992
Kurtosis470.3351746
Mean0.01397286385
Median Absolute Deviation (MAD)0.581347
Skewness10.15556663
Sum145.73697
Variance1.268428032
MonotonicityNot monotonic
2022-09-22T19:56:20.002175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.107265225
 
2.2%
-0.141884221
 
2.1%
0.024215220
 
2.1%
-0.058835214
 
2.1%
-0.474083210
 
2.0%
-0.224934203
 
1.9%
-0.307984198
 
1.9%
-0.349509194
 
1.9%
-0.10036194
 
1.9%
-0.432558193
 
1.9%
Other values (216)8358
80.1%
ValueCountFrequency (%)
-7.4502573
< 0.1%
-4.4189431
 
< 0.1%
-3.9206451
 
< 0.1%
-3.879121
 
< 0.1%
-3.8375952
< 0.1%
-3.7960711
 
< 0.1%
-3.7130211
 
< 0.1%
-3.6714962
< 0.1%
-3.5884472
< 0.1%
-3.5469221
 
< 0.1%
ValueCountFrequency (%)
531
< 0.1%
5.5054951
< 0.1%
5.0071961
< 0.1%
4.8826222
< 0.1%
4.7165231
< 0.1%
4.5919481
< 0.1%
4.5504231
< 0.1%
4.5088982
< 0.1%
4.3427991
< 0.1%
4.2597492
< 0.1%

interlinear_spacing
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct229
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.005605411505
Minimum-11.935457
Maximum83
Zeros0
Zeros (%)0.0%
Negative3099
Negative (%)29.7%
Memory size81.6 KiB
2022-09-22T19:56:20.902231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-11.935457
5-th percentile-1.591843
Q1-0.044076
median0.220177
Q30.446679
95-th percentile0.824183
Maximum83
Range94.935457
Interquartile range (IQR)0.490755

Descriptive statistics

Standard deviation1.313754284
Coefficient of variation (CV)234.3724958
Kurtosis1541.044209
Mean0.005605411505
Median Absolute Deviation (MAD)0.226503
Skewness22.13946265
Sum58.464442
Variance1.725950319
MonotonicityNot monotonic
2022-09-22T19:56:22.047263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.295677512
 
4.9%
0.144676473
 
4.5%
0.182426465
 
4.5%
0.333428451
 
4.3%
0.257927450
 
4.3%
0.220177433
 
4.2%
0.446679390
 
3.7%
0.371178388
 
3.7%
0.408929383
 
3.7%
0.069175381
 
3.7%
Other values (219)6104
58.5%
ValueCountFrequency (%)
-11.93545717
0.2%
-9.6704321
 
< 0.1%
-8.9909251
 
< 0.1%
-8.9531751
 
< 0.1%
-8.9154241
 
< 0.1%
-8.8399231
 
< 0.1%
-8.6889221
 
< 0.1%
-8.575671
 
< 0.1%
-8.0597481
 
< 0.1%
-7.1034041
 
< 0.1%
ValueCountFrequency (%)
831
< 0.1%
10.7147921
< 0.1%
8.9027721
< 0.1%
7.3927561
< 0.1%
5.127731
< 0.1%
4.5237241
< 0.1%
3.6177141
< 0.1%
3.5799631
< 0.1%
3.4667121
< 0.1%
3.3157112
< 0.1%

weight
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct10114
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01032255321
Minimum-4.247781
Maximum13.173081
Zeros0
Zeros (%)0.0%
Negative4684
Negative (%)44.9%
Memory size81.6 KiB
2022-09-22T19:56:22.622245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-4.247781
5-th percentile-1.7781679
Q1-0.5419915
median0.111803
Q30.65494425
95-th percentile1.443792
Maximum13.173081
Range17.420862
Interquartile range (IQR)1.19693575

Descriptive statistics

Standard deviation1.003507085
Coefficient of variation (CV)97.21500723
Kurtosis3.844505272
Mean0.01032255321
Median Absolute Deviation (MAD)0.592267
Skewness-0.3540198348
Sum107.66423
Variance1.00702647
MonotonicityNot monotonic
2022-09-22T19:56:23.144546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0086993
 
< 0.1%
0.5510863
 
< 0.1%
0.4892093
 
< 0.1%
-0.3894553
 
< 0.1%
0.2823963
 
< 0.1%
0.8059332
 
< 0.1%
0.4652512
 
< 0.1%
0.5875872
 
< 0.1%
-1.0489762
 
< 0.1%
0.5382142
 
< 0.1%
Other values (10104)10405
99.8%
ValueCountFrequency (%)
-4.2477811
< 0.1%
-4.1648191
< 0.1%
-4.0906911
< 0.1%
-4.0644611
< 0.1%
-4.0112621
< 0.1%
-3.9701411
< 0.1%
-3.9662191
< 0.1%
-3.9318541
< 0.1%
-3.8383491
< 0.1%
-3.8022171
< 0.1%
ValueCountFrequency (%)
13.1730811
< 0.1%
4.5108971
< 0.1%
3.9874391
< 0.1%
3.8177481
< 0.1%
3.5577361
< 0.1%
3.5568431
< 0.1%
3.5452521
< 0.1%
3.4797841
< 0.1%
3.4499621
< 0.1%
3.423791
< 0.1%

peak_number
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct261
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01291409904
Minimum-5.486218
Maximum44
Zeros0
Zeros (%)0.0%
Negative4593
Negative (%)44.0%
Memory size81.6 KiB
2022-09-22T19:56:23.741615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-5.486218
5-th percentile-1.900349
Q1-0.372457
median0.064084
Q30.500624
95-th percentile1.591976
Maximum44
Range49.486218
Interquartile range (IQR)0.873081

Descriptive statistics

Standard deviation1.087665481
Coefficient of variation (CV)84.22310198
Kurtosis257.6364546
Mean0.01291409904
Median Absolute Deviation (MAD)0.43654
Skewness5.669546108
Sum134.694053
Variance1.183016198
MonotonicityNot monotonic
2022-09-22T19:56:24.187881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.001721249
 
2.4%
0.126447241
 
2.3%
-0.060642239
 
2.3%
0.095265223
 
2.1%
0.18881221
 
2.1%
-0.154186220
 
2.1%
0.064084215
 
2.1%
0.157628212
 
2.0%
-0.123005205
 
2.0%
-0.029461205
 
2.0%
Other values (251)8200
78.6%
ValueCountFrequency (%)
-5.4862181
< 0.1%
-5.4238551
< 0.1%
-5.0496771
< 0.1%
-4.4884112
< 0.1%
-4.3948661
< 0.1%
-4.2389591
< 0.1%
-4.1765961
< 0.1%
-4.1142332
< 0.1%
-4.051872
< 0.1%
-4.0206891
< 0.1%
ValueCountFrequency (%)
441
 
< 0.1%
3.2445941
 
< 0.1%
3.1822311
 
< 0.1%
2.9639611
 
< 0.1%
2.9015982
< 0.1%
2.8704164
< 0.1%
2.8392352
< 0.1%
2.8080531
 
< 0.1%
2.7768721
 
< 0.1%
2.745693
< 0.1%

ratio
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9975
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0008177224353
Minimum-6.719324
Maximum4.671232
Zeros0
Zeros (%)0.0%
Negative5393
Negative (%)51.7%
Memory size81.6 KiB
2022-09-22T19:56:24.472713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-6.719324
5-th percentile-1.40464965
Q1-0.51609725
median-0.034513
Q30.53085475
95-th percentile1.61266485
Maximum4.671232
Range11.390556
Interquartile range (IQR)1.046952

Descriptive statistics

Standard deviation1.007093948
Coefficient of variation (CV)1231.584099
Kurtosis5.8080618
Mean0.0008177224353
Median Absolute Deviation (MAD)0.518865
Skewness-0.7257669311
Sum8.528845
Variance1.014238221
MonotonicityNot monotonic
2022-09-22T19:56:24.893031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.69175922
 
0.2%
-6.71932419
 
0.2%
0.8151327
 
0.1%
-0.1036985
 
< 0.1%
-0.3371945
 
< 0.1%
-0.0535485
 
< 0.1%
-0.0890025
 
< 0.1%
-0.2712284
 
< 0.1%
-0.6154624
 
< 0.1%
-1.194064
 
< 0.1%
Other values (9965)10350
99.2%
ValueCountFrequency (%)
-6.71932419
0.2%
-6.0923011
 
< 0.1%
-5.9079171
 
< 0.1%
-5.7147341
 
< 0.1%
-5.5432141
 
< 0.1%
-5.5341241
 
< 0.1%
-5.4761391
 
< 0.1%
-5.4604071
 
< 0.1%
-5.3876541
 
< 0.1%
-5.0794771
 
< 0.1%
ValueCountFrequency (%)
4.6712321
< 0.1%
4.4433291
< 0.1%
4.2813081
< 0.1%
4.0486921
< 0.1%
4.0346841
< 0.1%
4.033371
< 0.1%
3.9796041
< 0.1%
3.904261
< 0.1%
3.8718671
< 0.1%
3.7528471
< 0.1%

class
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size590.9 KiB
A
4286 
F
1961 
E
1095 
I
831 
X
522 
Other values (7)
1735 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10430
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowF

Common Values

ValueCountFrequency (%)
A4286
41.1%
F1961
18.8%
E1095
 
10.5%
I831
 
8.0%
X522
 
5.0%
H519
 
5.0%
G446
 
4.3%
D352
 
3.4%
Y266
 
2.6%
C103
 
1.0%
Other values (2)49
 
0.5%

Length

2022-09-22T19:56:25.168295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a4286
41.1%
f1961
18.8%
e1095
 
10.5%
i831
 
8.0%
x522
 
5.0%
h519
 
5.0%
g446
 
4.3%
d352
 
3.4%
y266
 
2.6%
c103
 
1.0%
Other values (2)49
 
0.5%

Most occurring characters

ValueCountFrequency (%)
A4286
41.1%
F1961
18.8%
E1095
 
10.5%
I831
 
8.0%
X522
 
5.0%
H519
 
5.0%
G446
 
4.3%
D352
 
3.4%
Y266
 
2.6%
C103
 
1.0%
Other values (2)49
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10430
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A4286
41.1%
F1961
18.8%
E1095
 
10.5%
I831
 
8.0%
X522
 
5.0%
H519
 
5.0%
G446
 
4.3%
D352
 
3.4%
Y266
 
2.6%
C103
 
1.0%
Other values (2)49
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin10430
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A4286
41.1%
F1961
18.8%
E1095
 
10.5%
I831
 
8.0%
X522
 
5.0%
H519
 
5.0%
G446
 
4.3%
D352
 
3.4%
Y266
 
2.6%
C103
 
1.0%
Other values (2)49
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII10430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A4286
41.1%
F1961
18.8%
E1095
 
10.5%
I831
 
8.0%
X522
 
5.0%
H519
 
5.0%
G446
 
4.3%
D352
 
3.4%
Y266
 
2.6%
C103
 
1.0%
Other values (2)49
 
0.5%

Interactions

2022-09-22T19:55:56.190226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:18.060683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:21.731877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:24.705056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:28.158167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:31.667957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:36.295156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:40.468941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:44.201386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:47.936206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:57.057204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:18.826530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:22.078279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:24.993330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:28.426840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:32.211482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:36.773219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:40.867177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:44.535413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:48.885170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:57.987425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:19.235328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:22.365422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:25.307966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:28.841694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:32.686225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:37.306381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:41.156842image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:44.836086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:49.699489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:58.737566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:19.546819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:22.621343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:25.603271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:29.151786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:33.274207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:37.595906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:41.383645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:45.144287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:50.569622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:59.444462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:19.845103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:22.891622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:25.902221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:29.413234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:33.864292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:38.030536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:41.666351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:45.544449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:51.445147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:56:00.439214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:20.151699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:23.168737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:26.246631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:29.664246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:34.202234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:38.363345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:42.120851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:45.995362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:52.224524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:56:00.815270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:20.534466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:23.437376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:26.712401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:30.294167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:34.514146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:38.831849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:42.448358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:46.322203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:53.009488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:56:01.707493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:20.830156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:23.690155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:27.200068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:30.709781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:35.100341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:39.059528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:42.960595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:46.665330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:53.732314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:56:02.729837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:21.158219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:23.979208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:27.665267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:30.998032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:35.706819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:39.709911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:43.727046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:46.994280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:54.604260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:56:03.424639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:21.426999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:24.300452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:27.946159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:31.325752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:36.020118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:39.978319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:43.966381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:47.513944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T19:55:55.356242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-09-22T19:56:25.534895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-22T19:56:26.338298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-22T19:56:27.065309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-22T19:56:28.457186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-22T19:56:04.337296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-22T19:56:05.862352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

intercolumnar_distanceupper_marginlower_marginexploitationrow_numbermodular_ratiointerlinear_spacingweightpeak_numberratioclass
00.266074-0.1656200.3209800.4832990.1723400.2733640.3711780.9298230.2511730.159345A
10.1302920.870736-3.2105280.0624930.2617181.4360601.4659400.6362030.2823540.515587A
2-0.1165850.0699150.068476-0.7831470.2617180.439463-0.081827-0.888236-0.1230050.582939A
30.0315410.297600-3.210528-0.583590-0.721442-0.3079840.7109321.0516930.594169-0.533994A
40.2290430.807926-0.0524420.0826340.2617180.1487900.6354310.0510620.032902-0.086652F
50.117948-0.220579-3.210528-1.6232380.261718-0.3495090.257927-0.385979-0.247731-0.331310A
60.389513-0.220579-3.210528-2.6241550.261718-0.7647570.484429-0.597510-0.372457-0.810261A
70.019197-0.0400010.288973-0.0425970.261718-1.0139060.0691750.8907010.095265-0.842014F
80.5006070.1405760.388552-0.6373580.261718-0.6817070.2956770.9310460.500624-0.642297H
9-0.2523670.0699150.2462960.5235500.261718-1.2215300.8996841.3730760.625350-1.400890E

Last rows

intercolumnar_distanceupper_marginlower_marginexploitationrow_numbermodular_ratiointerlinear_spacingweightpeak_numberratioclass
10420-0.0054900.4781770.029355-0.2476440.1723400.6055630.673182-0.951919-0.5283640.286973A
104210.2413860.2347900.1218221.0379880.2617180.6470880.1824260.6849360.2199910.628422A
10422-0.277055-0.251983-3.2034151.9579260.2617181.8928330.6354311.8982052.1844241.427425X
104234.969080-0.3854530.143160-2.6007320.976743-0.764757-0.232828-2.348488-1.183175-0.459372I
104240.2166990.3211530.1289350.4910870.2617180.4394630.0691750.2528460.1888100.482857A
104250.0809160.5880930.0151300.0022500.261718-0.5571330.3711780.9323460.282354-0.580141F
104260.253730-0.3383460.352988-1.1542430.172340-0.5571330.2579270.3484280.032902-0.527134F
104270.229043-0.0007450.171611-0.0027930.2617180.6886130.295677-1.088486-0.5907270.580142A
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